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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code>.KNeighborsClassifier</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-neighbors-kneighborsclassifier">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.neighbors.KNeighborsClassifier</span></code></a></li>
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  <div class="section" id="sklearn-neighbors-kneighborsclassifier">
<h1><a class="reference internal" href="../classes.html#module-sklearn.neighbors" title="sklearn.neighbors"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code></a>.KNeighborsClassifier<a class="headerlink" href="#sklearn-neighbors-kneighborsclassifier" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.neighbors.KNeighborsClassifier">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.neighbors.</code><code class="sig-name descname">KNeighborsClassifier</code><span class="sig-paren">(</span><em class="sig-param">n_neighbors=5</em>, <em class="sig-param">weights='uniform'</em>, <em class="sig-param">algorithm='auto'</em>, <em class="sig-param">leaf_size=30</em>, <em class="sig-param">p=2</em>, <em class="sig-param">metric='minkowski'</em>, <em class="sig-param">metric_params=None</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_classification.py#L25"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier" title="Permalink to this definition">¶</a></dt>
<dd><p>Classifier implementing the k-nearest neighbors vote.</p>
<p>Read more in the <a class="reference internal" href="../neighbors.html#classification"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>n_neighbors</strong><span class="classifier">int, optional (default = 5)</span></dt><dd><p>Number of neighbors to use by default for <a class="reference internal" href="#sklearn.neighbors.KNeighborsClassifier.kneighbors" title="sklearn.neighbors.KNeighborsClassifier.kneighbors"><code class="xref py py-meth docutils literal notranslate"><span class="pre">kneighbors</span></code></a> queries.</p>
</dd>
<dt><strong>weights</strong><span class="classifier">str or callable, optional (default = ‘uniform’)</span></dt><dd><p>weight function used in prediction.  Possible values:</p>
<ul class="simple">
<li><p>‘uniform’ : uniform weights.  All points in each neighborhood
are weighted equally.</p></li>
<li><p>‘distance’ : weight points by the inverse of their distance.
in this case, closer neighbors of a query point will have a
greater influence than neighbors which are further away.</p></li>
<li><p>[callable] : a user-defined function which accepts an
array of distances, and returns an array of the same shape
containing the weights.</p></li>
</ul>
</dd>
<dt><strong>algorithm</strong><span class="classifier">{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional</span></dt><dd><p>Algorithm used to compute the nearest neighbors:</p>
<ul class="simple">
<li><p>‘ball_tree’ will use <a class="reference internal" href="sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree" title="sklearn.neighbors.BallTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">BallTree</span></code></a></p></li>
<li><p>‘kd_tree’ will use <a class="reference internal" href="sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree" title="sklearn.neighbors.KDTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">KDTree</span></code></a></p></li>
<li><p>‘brute’ will use a brute-force search.</p></li>
<li><p>‘auto’ will attempt to decide the most appropriate algorithm
based on the values passed to <a class="reference internal" href="#sklearn.neighbors.KNeighborsClassifier.fit" title="sklearn.neighbors.KNeighborsClassifier.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> method.</p></li>
</ul>
<p>Note: fitting on sparse input will override the setting of
this parameter, using brute force.</p>
</dd>
<dt><strong>leaf_size</strong><span class="classifier">int, optional (default = 30)</span></dt><dd><p>Leaf size passed to BallTree or KDTree.  This can affect the
speed of the construction and query, as well as the memory
required to store the tree.  The optimal value depends on the
nature of the problem.</p>
</dd>
<dt><strong>p</strong><span class="classifier">integer, optional (default = 2)</span></dt><dd><p>Power parameter for the Minkowski metric. When p = 1, this is
equivalent to using manhattan_distance (l1), and euclidean_distance
(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.</p>
</dd>
<dt><strong>metric</strong><span class="classifier">string or callable, default ‘minkowski’</span></dt><dd><p>the distance metric to use for the tree.  The default metric is
minkowski, and with p=2 is equivalent to the standard Euclidean
metric. See the documentation of the DistanceMetric class for a
list of available metrics.
If metric is “precomputed”, X is assumed to be a distance matrix and
must be square during fit. X may be a <a class="reference internal" href="../../glossary.html#term-sparse-graph"><span class="xref std std-term">Glossary</span></a>,
in which case only “nonzero” elements may be considered neighbors.</p>
</dd>
<dt><strong>metric_params</strong><span class="classifier">dict, optional (default = None)</span></dt><dd><p>Additional keyword arguments for the metric function.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, optional (default=None)</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend" title="(in joblib v0.14.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n-jobs"><span class="xref std std-term">Glossary</span></a>
for more details.
Doesn’t affect <a class="reference internal" href="#sklearn.neighbors.KNeighborsClassifier.fit" title="sklearn.neighbors.KNeighborsClassifier.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> method.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>classes_</strong><span class="classifier">array of shape (n_classes,)</span></dt><dd><p>Class labels known to the classifier</p>
</dd>
<dt><strong>effective_metric_</strong><span class="classifier">string or callble</span></dt><dd><p>The distance metric used. It will be same as the <code class="docutils literal notranslate"><span class="pre">metric</span></code> parameter
or a synonym of it, e.g. ‘euclidean’ if the <code class="docutils literal notranslate"><span class="pre">metric</span></code> parameter set to
‘minkowski’ and <code class="docutils literal notranslate"><span class="pre">p</span></code> parameter set to 2.</p>
</dd>
<dt><strong>effective_metric_params_</strong><span class="classifier">dict</span></dt><dd><p>Additional keyword arguments for the metric function. For most metrics
will be same with <code class="docutils literal notranslate"><span class="pre">metric_params</span></code> parameter, but may also contain the
<code class="docutils literal notranslate"><span class="pre">p</span></code> parameter value if the <code class="docutils literal notranslate"><span class="pre">effective_metric_</span></code> attribute is set to
‘minkowski’.</p>
</dd>
<dt><strong>outputs_2d_</strong><span class="classifier">bool</span></dt><dd><p>False when <code class="docutils literal notranslate"><span class="pre">y</span></code>’s shape is (n_samples, ) or (n_samples, 1) during fit
otherwise True.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.neighbors.RadiusNeighborsClassifier.html#sklearn.neighbors.RadiusNeighborsClassifier" title="sklearn.neighbors.RadiusNeighborsClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RadiusNeighborsClassifier</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.neighbors.KNeighborsRegressor.html#sklearn.neighbors.KNeighborsRegressor" title="sklearn.neighbors.KNeighborsRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KNeighborsRegressor</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.neighbors.RadiusNeighborsRegressor.html#sklearn.neighbors.RadiusNeighborsRegressor" title="sklearn.neighbors.RadiusNeighborsRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RadiusNeighborsRegressor</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors" title="sklearn.neighbors.NearestNeighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NearestNeighbors</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>See <a class="reference internal" href="../neighbors.html#neighbors"><span class="std std-ref">Nearest Neighbors</span></a> in the online documentation
for a discussion of the choice of <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> and <code class="docutils literal notranslate"><span class="pre">leaf_size</span></code>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Regarding the Nearest Neighbors algorithms, if it is found that two
neighbors, neighbor <code class="docutils literal notranslate"><span class="pre">k+1</span></code> and <code class="docutils literal notranslate"><span class="pre">k</span></code>, have identical distances
but different labels, the results will depend on the ordering of the
training data.</p>
</div>
<p><a class="reference external" href="https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm">https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm</a></p>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">KNeighborsClassifier(...)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">neigh</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mf">1.1</span><span class="p">]]))</span>
<span class="go">[0]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">neigh</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">([[</span><span class="mf">0.9</span><span class="p">]]))</span>
<span class="go">[[0.66666667 0.33333333]]</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsClassifier.fit" title="sklearn.neighbors.KNeighborsClassifier.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X, y)</p></td>
<td><p>Fit the model using X as training data and y as target values</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsClassifier.get_params" title="sklearn.neighbors.KNeighborsClassifier.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsClassifier.kneighbors" title="sklearn.neighbors.KNeighborsClassifier.kneighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kneighbors</span></code></a>(self[, X, n_neighbors, …])</p></td>
<td><p>Finds the K-neighbors of a point.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsClassifier.kneighbors_graph" title="sklearn.neighbors.KNeighborsClassifier.kneighbors_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kneighbors_graph</span></code></a>(self[, X, n_neighbors, mode])</p></td>
<td><p>Computes the (weighted) graph of k-Neighbors for points in X</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsClassifier.predict" title="sklearn.neighbors.KNeighborsClassifier.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(self, X)</p></td>
<td><p>Predict the class labels for the provided data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsClassifier.predict_proba" title="sklearn.neighbors.KNeighborsClassifier.predict_proba"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_proba</span></code></a>(self, X)</p></td>
<td><p>Return probability estimates for the test data X.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsClassifier.score" title="sklearn.neighbors.KNeighborsClassifier.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(self, X, y[, sample_weight])</p></td>
<td><p>Return the mean accuracy on the given test data and labels.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsClassifier.set_params" title="sklearn.neighbors.KNeighborsClassifier.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.neighbors.KNeighborsClassifier.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">n_neighbors=5</em>, <em class="sig-param">weights='uniform'</em>, <em class="sig-param">algorithm='auto'</em>, <em class="sig-param">leaf_size=30</em>, <em class="sig-param">p=2</em>, <em class="sig-param">metric='minkowski'</em>, <em class="sig-param">metric_params=None</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_classification.py#L145"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsClassifier.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L1116"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the model using X as training data and y as target values</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix, BallTree, KDTree}</span></dt><dd><p>Training data. If array or matrix, shape [n_samples, n_features],
or [n_samples, n_samples] if metric=’precomputed’.</p>
</dd>
<dt><strong>y</strong><span class="classifier">{array-like, sparse matrix}</span></dt><dd><p>Target values of shape = [n_samples] or [n_samples, n_outputs]</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsClassifier.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsClassifier.kneighbors">
<code class="sig-name descname">kneighbors</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X=None</em>, <em class="sig-param">n_neighbors=None</em>, <em class="sig-param">return_distance=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L531"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.kneighbors" title="Permalink to this definition">¶</a></dt>
<dd><p>Finds the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == ‘precomputed’</span></dt><dd><p>The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.</p>
</dd>
<dt><strong>n_neighbors</strong><span class="classifier">int</span></dt><dd><p>Number of neighbors to get (default is the value
passed to the constructor).</p>
</dd>
<dt><strong>return_distance</strong><span class="classifier">boolean, optional. Defaults to True.</span></dt><dd><p>If False, distances will not be returned</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>neigh_dist</strong><span class="classifier">array, shape (n_queries, n_neighbors)</span></dt><dd><p>Array representing the lengths to points, only present if
return_distance=True</p>
</dd>
<dt><strong>neigh_ind</strong><span class="classifier">array, shape (n_queries, n_neighbors)</span></dt><dd><p>Indices of the nearest points in the population matrix.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<p>In the following example, we construct a NeighborsClassifier
class from an array representing our data set and ask who’s
the closest point to [1,1,1]</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">samples</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">NearestNeighbors</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
<span class="go">NearestNeighbors(n_neighbors=1)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]))</span>
<span class="go">(array([[0.5]]), array([[2]]))</span>
</pre></div>
</div>
<p>As you can see, it returns [[0.5]], and [[2]], which means that the
element is at distance 0.5 and is the third element of samples
(indexes start at 0). You can also query for multiple points:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">return_distance</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="go">array([[1],</span>
<span class="go">       [2]]...)</span>
</pre></div>
</div>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsClassifier.kneighbors_graph">
<code class="sig-name descname">kneighbors_graph</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X=None</em>, <em class="sig-param">n_neighbors=None</em>, <em class="sig-param">mode='connectivity'</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L705"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.kneighbors_graph" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the (weighted) graph of k-Neighbors for points in X</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == ‘precomputed’</span></dt><dd><p>The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.</p>
</dd>
<dt><strong>n_neighbors</strong><span class="classifier">int</span></dt><dd><p>Number of neighbors for each sample.
(default is value passed to the constructor).</p>
</dd>
<dt><strong>mode</strong><span class="classifier">{‘connectivity’, ‘distance’}, optional</span></dt><dd><p>Type of returned matrix: ‘connectivity’ will return the
connectivity matrix with ones and zeros, in ‘distance’ the
edges are Euclidean distance between points.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>A</strong><span class="classifier">sparse graph in CSR format, shape = [n_queries, n_samples_fit]</span></dt><dd><p>n_samples_fit is the number of samples in the fitted data
A[i, j] is assigned the weight of edge that connects i to j.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors.radius_neighbors_graph" title="sklearn.neighbors.NearestNeighbors.radius_neighbors_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NearestNeighbors.radius_neighbors_graph</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">NearestNeighbors</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">NearestNeighbors(n_neighbors=2)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">A</span> <span class="o">=</span> <span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors_graph</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">A</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
<span class="go">array([[1., 0., 1.],</span>
<span class="go">       [0., 1., 1.],</span>
<span class="go">       [1., 0., 1.]])</span>
</pre></div>
</div>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsClassifier.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_classification.py#L157"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict the class labels for the provided data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == ‘precomputed’</span></dt><dd><p>Test samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y</strong><span class="classifier">array of shape [n_queries] or [n_queries, n_outputs]</span></dt><dd><p>Class labels for each data sample.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsClassifier.predict_proba">
<code class="sig-name descname">predict_proba</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_classification.py#L199"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.predict_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Return probability estimates for the test data X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == ‘precomputed’</span></dt><dd><p>Test samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>p</strong><span class="classifier">array of shape = [n_queries, n_classes], or a list of n_outputs</span></dt><dd><p>of such arrays if n_outputs &gt; 1.
The class probabilities of the input samples. Classes are ordered
by lexicographic order.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsClassifier.score">
<code class="sig-name descname">score</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L344"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the mean accuracy on the given test data and labels.</p>
<p>In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True labels for X.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>Mean accuracy of self.predict(X) wrt. y.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsClassifier.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-neighbors-kneighborsclassifier">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.neighbors.KNeighborsClassifier</span></code><a class="headerlink" href="#examples-using-sklearn-neighbors-kneighborsclassifier" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this ..."><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_classifier_comparison_thumb.png" src="../../_images/sphx_glr_plot_classifier_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py"><span class="std std-ref">Classifier comparison</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_plot_voting_decision_regions_thumb.png" src="../../_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Sample usage of Nearest Neighbors classification. It will plot the decision boundaries for each..."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_plot_classification_thumb.png" src="../../_images/sphx_glr_plot_classification_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py"><span class="std std-ref">Nearest Neighbors Classification</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This examples demonstrates how to precompute the k nearest neighbors before using them in KNeig..."><div class="figure align-default" id="id4">
<img alt="../../_images/sphx_glr_plot_caching_nearest_neighbors_thumb.png" src="../../_images/sphx_glr_plot_caching_nearest_neighbors_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_caching_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-plot-caching-nearest-neighbors-py"><span class="std std-ref">Caching nearest neighbors</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example comparing nearest neighbors classification with and without Neighborhood Components ..."><div class="figure align-default" id="id5">
<img alt="../../_images/sphx_glr_plot_nca_classification_thumb.png" src="../../_images/sphx_glr_plot_nca_classification_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_nca_classification.html#sphx-glr-auto-examples-neighbors-plot-nca-classification-py"><span class="std std-ref">Comparing Nearest Neighbors with and without Neighborhood Components Analysis</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Sample usage of Neighborhood Components Analysis for dimensionality reduction."><div class="figure align-default" id="id6">
<img alt="../../_images/sphx_glr_plot_nca_dim_reduction_thumb.png" src="../../_images/sphx_glr_plot_nca_dim_reduction_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_nca_dim_reduction.html#sphx-glr-auto-examples-neighbors-plot-nca-dim-reduction-py"><span class="std std-ref">Dimensionality Reduction with Neighborhood Components Analysis</span></a></span><a class="headerlink" href="#id6" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A tutorial exercise regarding the use of classification techniques on the Digits dataset."><div class="figure align-default" id="id7">
<img alt="../../_images/sphx_glr_plot_digits_classification_exercise_thumb.png" src="../../_images/sphx_glr_plot_digits_classification_exercise_thumb.png" />
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</div><div class="sphx-glr-thumbcontainer" tooltip="This is an example showing how scikit-learn can be used to classify documents by topics using a..."><div class="figure align-default" id="id8">
<img alt="../../_images/sphx_glr_plot_document_classification_20newsgroups_thumb.png" src="../../_images/sphx_glr_plot_document_classification_20newsgroups_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/text/plot_document_classification_20newsgroups.html#sphx-glr-auto-examples-text-plot-document-classification-20newsgroups-py"><span class="std std-ref">Classification of text documents using sparse features</span></a></span><a class="headerlink" href="#id8" title="Permalink to this image">¶</a></p>
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